Synopses & Reviews
Introduction to the basic concepts of probability theory: independence, expectation, convergence in law and almost-sure convergence. Short expositions of more advanced topics such as Markov Chains, Stochastic Processes, Bayesian Decision Theory and Information Theory.
"""About to open a new book on probability theory, one is gripped by a premonition of deja 'goute'. What, after all, can possibly be offered that has not been consumed many times before? In this text the author manages remarkably well to avoid just serving up the same old fare. True, the standard dishes are still there, probability spaces, random variables, moments and convergence, enriched with Poisson and Gaussian processes; but very well presented, the ingredients selected and prepared with considerable taste. In addition, though, there are the numerous entremets-the Hardy-Weinberg theorem, an introduction to information theory, tests of Gaussian hypotheses, signal theory and many more-which can hardly fail to arouse a lively appreciation, even from the dullest palate. Three stars-vaut le voyage."" Mathematical Reviews"
Table of Contents
Preface.- Abbreviations and Notations.- Basic Concepts and Elementary Models.- Discrete Probability.- Probability Densities.- Gaus and Poisson.- Convergences.- Additional Exercises.- Solutions to Additional Exercises.- Index.